Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations45211
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 MiB
Average record size in memory144.0 B

Variable types

Numeric8
Text10

Alerts

pdays is highly overall correlated with previousHigh correlation
previous is highly overall correlated with pdaysHigh correlation
previous is highly skewed (γ1 = 41.84645447) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
balance has 3514 (7.8%) zeros Zeros
previous has 36954 (81.7%) zeros Zeros

Reproduction

Analysis started2025-02-16 22:09:38.279678
Analysis finished2025-02-16 22:09:40.353618
Duration2.07 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct45211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22605
Minimum0
Maximum45210
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.375615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2260.5
Q111302.5
median22605
Q333907.5
95-th percentile42949.5
Maximum45210
Range45210
Interquartile range (IQR)22605

Descriptive statistics

Standard deviation13051.436
Coefficient of variation (CV)0.57736942
Kurtosis-1.2
Mean22605
Median Absolute Deviation (MAD)11303
Skewness0
Sum1.0219947 × 109
Variance1.7033998 × 108
MonotonicityStrictly increasing
2025-02-16T23:09:40.404352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
30199 1
 
< 0.1%
30135 1
 
< 0.1%
30136 1
 
< 0.1%
30137 1
 
< 0.1%
30138 1
 
< 0.1%
30139 1
 
< 0.1%
30140 1
 
< 0.1%
30141 1
 
< 0.1%
30142 1
 
< 0.1%
Other values (45201) 45201
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
45210 1
< 0.1%
45209 1
< 0.1%
45208 1
< 0.1%
45207 1
< 0.1%
45206 1
< 0.1%
45205 1
< 0.1%
45204 1
< 0.1%
45203 1
< 0.1%
45202 1
< 0.1%
45201 1
< 0.1%

age
Real number (ℝ)

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.93621
Minimum18
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.432639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q133
median39
Q348
95-th percentile59
Maximum95
Range77
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.618762
Coefficient of variation (CV)0.25939778
Kurtosis0.31957038
Mean40.93621
Median Absolute Deviation (MAD)7
Skewness0.68481793
Sum1850767
Variance112.75811
MonotonicityNot monotonic
2025-02-16T23:09:40.461544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 2085
 
4.6%
31 1996
 
4.4%
33 1972
 
4.4%
34 1930
 
4.3%
35 1894
 
4.2%
36 1806
 
4.0%
30 1757
 
3.9%
37 1696
 
3.8%
39 1487
 
3.3%
38 1466
 
3.2%
Other values (67) 27122
60.0%
ValueCountFrequency (%)
18 12
 
< 0.1%
19 35
 
0.1%
20 50
 
0.1%
21 79
 
0.2%
22 129
 
0.3%
23 202
 
0.4%
24 302
 
0.7%
25 527
1.2%
26 805
1.8%
27 909
2.0%
ValueCountFrequency (%)
95 2
 
< 0.1%
94 1
 
< 0.1%
93 2
 
< 0.1%
92 2
 
< 0.1%
90 2
 
< 0.1%
89 3
 
< 0.1%
88 2
 
< 0.1%
87 4
< 0.1%
86 9
< 0.1%
85 5
< 0.1%

job
Text

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.508763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.4855456
Min length6

Characters and Unicode

Total characters428851
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmanagement
2nd rowtechnician
3rd rowentrepreneur
4th rowblue-collar
5th rowunknown
ValueCountFrequency (%)
blue-collar 9732
21.5%
management 9458
20.9%
technician 7597
16.8%
admin 5171
11.4%
services 4154
9.2%
retired 2264
 
5.0%
self-employed 1579
 
3.5%
entrepreneur 1487
 
3.3%
unemployed 1303
 
2.9%
housemaid 1240
 
2.7%
Other values (2) 1226
 
2.7%
2025-02-16T23:09:40.575932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 428851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 428851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 428851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64550
15.1%
n 45360
10.6%
a 42656
9.9%
l 33657
 
7.8%
c 29080
 
6.8%
m 28209
 
6.6%
i 28023
 
6.5%
r 22875
 
5.3%
t 22682
 
5.3%
u 14988
 
3.5%
Other values (14) 96771
22.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.605816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.8322753
Min length6

Characters and Unicode

Total characters308894
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowsingle
ValueCountFrequency (%)
married 27214
60.2%
single 12790
28.3%
divorced 5207
 
11.5%
2025-02-16T23:09:40.660680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 308894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 308894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 308894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 59635
19.3%
i 45211
14.6%
e 45211
14.6%
d 37628
12.2%
m 27214
8.8%
a 27214
8.8%
s 12790
 
4.1%
n 12790
 
4.1%
g 12790
 
4.1%
l 12790
 
4.1%
Other values (3) 15621
 
5.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.691885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.3205857
Min length7

Characters and Unicode

Total characters376182
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtertiary
2nd rowsecondary
3rd rowsecondary
4th rowunknown
5th rowunknown
ValueCountFrequency (%)
secondary 23202
51.3%
tertiary 13301
29.4%
primary 6851
 
15.2%
unknown 1857
 
4.1%
2025-02-16T23:09:40.748643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 376182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 376182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 376182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 63506
16.9%
a 43354
11.5%
y 43354
11.5%
e 36503
9.7%
n 28773
7.6%
t 26602
7.1%
o 25059
 
6.7%
s 23202
 
6.2%
c 23202
 
6.2%
d 23202
 
6.2%
Other values (6) 39425
10.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.763997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0180266
Min length2

Characters and Unicode

Total characters91237
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 44396
98.2%
yes 815
 
1.8%
2025-02-16T23:09:40.801449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 44396
48.7%
o 44396
48.7%
y 815
 
0.9%
e 815
 
0.9%
s 815
 
0.9%

balance
Real number (ℝ)

Zeros 

Distinct7168
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1362.2721
Minimum-8019
Maximum102127
Zeros3514
Zeros (%)7.8%
Negative3766
Negative (%)8.3%
Memory size353.3 KiB
2025-02-16T23:09:40.827353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-8019
5-th percentile-172
Q172
median448
Q31428
95-th percentile5768
Maximum102127
Range110146
Interquartile range (IQR)1356

Descriptive statistics

Standard deviation3044.7658
Coefficient of variation (CV)2.2350644
Kurtosis140.75155
Mean1362.2721
Median Absolute Deviation (MAD)448
Skewness8.3603083
Sum61589682
Variance9270599
MonotonicityNot monotonic
2025-02-16T23:09:40.858407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3514
 
7.8%
1 195
 
0.4%
2 156
 
0.3%
4 139
 
0.3%
3 134
 
0.3%
5 113
 
0.2%
6 88
 
0.2%
8 81
 
0.2%
23 75
 
0.2%
7 69
 
0.2%
Other values (7158) 40647
89.9%
ValueCountFrequency (%)
-8019 1
< 0.1%
-6847 1
< 0.1%
-4057 1
< 0.1%
-3372 1
< 0.1%
-3313 1
< 0.1%
-3058 1
< 0.1%
-2827 1
< 0.1%
-2712 1
< 0.1%
-2604 1
< 0.1%
-2282 1
< 0.1%
ValueCountFrequency (%)
102127 1
< 0.1%
98417 1
< 0.1%
81204 2
< 0.1%
71188 1
< 0.1%
66721 1
< 0.1%
66653 1
< 0.1%
64343 1
< 0.1%
59649 1
< 0.1%
58932 1
< 0.1%
58544 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.883823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.5558382
Min length2

Characters and Unicode

Total characters115552
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowno
ValueCountFrequency (%)
yes 25130
55.6%
no 20081
44.4%
2025-02-16T23:09:40.926599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 25130
21.7%
e 25130
21.7%
s 25130
21.7%
n 20081
17.4%
o 20081
17.4%

loan
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:40.943226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1602265
Min length2

Characters and Unicode

Total characters97666
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno
ValueCountFrequency (%)
no 37967
84.0%
yes 7244
 
16.0%
2025-02-16T23:09:40.982942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 37967
38.9%
o 37967
38.9%
y 7244
 
7.4%
e 7244
 
7.4%
s 7244
 
7.4%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.010210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.7762934
Min length7

Characters and Unicode

Total characters351574
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown
ValueCountFrequency (%)
cellular 29285
64.8%
unknown 13020
28.8%
telephone 2906
 
6.4%
2025-02-16T23:09:41.106351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 90761
25.8%
u 42305
12.0%
n 41966
11.9%
e 38003
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15926
 
4.5%
k 13020
 
3.7%
w 13020
 
3.7%
Other values (3) 8718
 
2.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.806419
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.127445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median16
Q321
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3224762
Coefficient of variation (CV)0.52652509
Kurtosis-1.0598974
Mean15.806419
Median Absolute Deviation (MAD)7
Skewness0.093079014
Sum714624
Variance69.263609
MonotonicityNot monotonic
2025-02-16T23:09:41.151166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 2752
 
6.1%
18 2308
 
5.1%
21 2026
 
4.5%
17 1939
 
4.3%
6 1932
 
4.3%
5 1910
 
4.2%
14 1848
 
4.1%
8 1842
 
4.1%
28 1830
 
4.0%
7 1817
 
4.0%
Other values (21) 25007
55.3%
ValueCountFrequency (%)
1 322
 
0.7%
2 1293
2.9%
3 1079
2.4%
4 1445
3.2%
5 1910
4.2%
6 1932
4.3%
7 1817
4.0%
8 1842
4.1%
9 1561
3.5%
10 524
 
1.2%
ValueCountFrequency (%)
31 643
 
1.4%
30 1566
3.5%
29 1745
3.9%
28 1830
4.0%
27 1121
2.5%
26 1035
2.3%
25 840
1.9%
24 447
 
1.0%
23 939
2.1%
22 905
2.0%

month
Text

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.179935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters135633
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay
ValueCountFrequency (%)
may 13766
30.4%
jul 6895
15.3%
aug 6247
13.8%
jun 5341
 
11.8%
nov 3970
 
8.8%
apr 2932
 
6.5%
feb 2649
 
5.9%
jan 1403
 
3.1%
oct 738
 
1.6%
sep 579
 
1.3%
Other values (2) 691
 
1.5%
2025-02-16T23:09:41.227759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24825
18.3%
u 18483
13.6%
m 14243
10.5%
y 13766
10.1%
j 13639
10.1%
n 10714
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4708
 
3.5%
v 3970
 
2.9%
Other values (9) 18143
13.4%

duration
Real number (ℝ)

Distinct1573
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.16308
Minimum0
Maximum4918
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.254414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1103
median180
Q3319
95-th percentile751
Maximum4918
Range4918
Interquartile range (IQR)216

Descriptive statistics

Standard deviation257.52781
Coefficient of variation (CV)0.99753928
Kurtosis18.153915
Mean258.16308
Median Absolute Deviation (MAD)93
Skewness3.1443181
Sum11671811
Variance66320.574
MonotonicityNot monotonic
2025-02-16T23:09:41.284736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 188
 
0.4%
90 184
 
0.4%
89 177
 
0.4%
104 175
 
0.4%
122 175
 
0.4%
114 175
 
0.4%
136 174
 
0.4%
139 174
 
0.4%
112 174
 
0.4%
121 173
 
0.4%
Other values (1563) 43442
96.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 15
 
< 0.1%
5 35
0.1%
6 45
0.1%
7 73
0.2%
8 85
0.2%
9 77
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
3881 1
< 0.1%
3785 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%
3253 1
< 0.1%
3183 1
< 0.1%
3102 1
< 0.1%

campaign
Real number (ℝ)

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7638407
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.312903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum63
Range62
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0980209
Coefficient of variation (CV)1.1209115
Kurtosis39.249651
Mean2.7638407
Median Absolute Deviation (MAD)1
Skewness4.8986502
Sum124956
Variance9.5977334
MonotonicityNot monotonic
2025-02-16T23:09:41.341849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 17544
38.8%
2 12505
27.7%
3 5521
 
12.2%
4 3522
 
7.8%
5 1764
 
3.9%
6 1291
 
2.9%
7 735
 
1.6%
8 540
 
1.2%
9 327
 
0.7%
10 266
 
0.6%
Other values (38) 1196
 
2.6%
ValueCountFrequency (%)
1 17544
38.8%
2 12505
27.7%
3 5521
 
12.2%
4 3522
 
7.8%
5 1764
 
3.9%
6 1291
 
2.9%
7 735
 
1.6%
8 540
 
1.2%
9 327
 
0.7%
10 266
 
0.6%
ValueCountFrequency (%)
63 1
 
< 0.1%
58 1
 
< 0.1%
55 1
 
< 0.1%
51 1
 
< 0.1%
50 2
< 0.1%
46 1
 
< 0.1%
44 1
 
< 0.1%
43 3
< 0.1%
41 2
< 0.1%
39 1
 
< 0.1%

pdays
Real number (ℝ)

High correlation 

Distinct559
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.197828
Minimum-1
Maximum871
Zeros0
Zeros (%)0.0%
Negative36954
Negative (%)81.7%
Memory size353.3 KiB
2025-02-16T23:09:41.370791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile317
Maximum871
Range872
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.12875
Coefficient of variation (CV)2.4908994
Kurtosis6.9351952
Mean40.197828
Median Absolute Deviation (MAD)0
Skewness2.6157155
Sum1817384
Variance10025.766
MonotonicityNot monotonic
2025-02-16T23:09:41.401322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 36954
81.7%
182 167
 
0.4%
92 147
 
0.3%
91 126
 
0.3%
183 126
 
0.3%
181 117
 
0.3%
370 99
 
0.2%
184 85
 
0.2%
364 77
 
0.2%
95 74
 
0.2%
Other values (549) 7239
 
16.0%
ValueCountFrequency (%)
-1 36954
81.7%
1 15
 
< 0.1%
2 37
 
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 11
 
< 0.1%
6 10
 
< 0.1%
7 7
 
< 0.1%
8 25
 
0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
871 1
< 0.1%
854 1
< 0.1%
850 1
< 0.1%
842 1
< 0.1%
838 1
< 0.1%
831 1
< 0.1%
828 1
< 0.1%
826 1
< 0.1%
808 1
< 0.1%
805 1
< 0.1%

previous
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58032337
Minimum0
Maximum275
Zeros36954
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.427701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum275
Range275
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.303441
Coefficient of variation (CV)3.9692371
Kurtosis4506.8607
Mean0.58032337
Median Absolute Deviation (MAD)0
Skewness41.846454
Sum26237
Variance5.3058406
MonotonicityNot monotonic
2025-02-16T23:09:41.453273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 36954
81.7%
1 2772
 
6.1%
2 2106
 
4.7%
3 1142
 
2.5%
4 714
 
1.6%
5 459
 
1.0%
6 277
 
0.6%
7 205
 
0.5%
8 129
 
0.3%
9 92
 
0.2%
Other values (31) 361
 
0.8%
ValueCountFrequency (%)
0 36954
81.7%
1 2772
 
6.1%
2 2106
 
4.7%
3 1142
 
2.5%
4 714
 
1.6%
5 459
 
1.0%
6 277
 
0.6%
7 205
 
0.5%
8 129
 
0.3%
9 92
 
0.2%
ValueCountFrequency (%)
275 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
41 1
< 0.1%
40 1
< 0.1%
38 2
< 0.1%
37 2
< 0.1%
35 1
< 0.1%
32 1
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.484111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9186039
Min length5

Characters and Unicode

Total characters312797
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown
ValueCountFrequency (%)
unknown 36959
81.7%
failure 4901
 
10.8%
other 1840
 
4.1%
success 1511
 
3.3%
2025-02-16T23:09:41.535326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 312797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 312797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 312797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 110877
35.4%
u 43371
 
13.9%
o 38799
 
12.4%
k 36959
 
11.8%
w 36959
 
11.8%
e 8252
 
2.6%
r 6741
 
2.2%
f 4901
 
1.6%
a 4901
 
1.6%
i 4901
 
1.6%
Other values (5) 16136
 
5.2%

y
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.3 KiB
2025-02-16T23:09:41.550635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1169848
Min length2

Characters and Unicode

Total characters95711
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 39922
88.3%
yes 5289
 
11.7%
2025-02-16T23:09:41.587170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 39922
41.7%
o 39922
41.7%
y 5289
 
5.5%
e 5289
 
5.5%
s 5289
 
5.5%

Interactions

2025-02-16T23:09:39.955428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.531727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.731322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.932035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.131233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.366393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.563600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.767439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.979711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.558034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.757782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.956498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.155196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.390037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.588040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.790302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:40.003769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.583269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.783152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.981915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.179792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.414335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.615097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.815799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:40.027675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.607142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.808510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.006899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.203830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.439061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.640211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.839411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:40.050960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.631951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.832695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.032194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.271332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.464209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.667205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.864208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:40.074247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.656085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.857952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.057679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.295225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.489372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.692285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.887545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:40.097317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.683964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.882941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.082888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.319270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.514459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.718057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.910815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:40.120214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.708441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:38.907406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.107304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.342416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.538027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.742241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T23:09:39.932942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-16T23:09:41.605323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Unnamed: 0agebalancecampaigndaydurationpdaysprevious
Unnamed: 01.000-0.0340.132-0.112-0.0640.0290.4940.496
age-0.0341.0000.0960.037-0.009-0.033-0.017-0.012
balance0.1320.0961.000-0.0310.0010.0430.0700.080
campaign-0.1120.037-0.0311.0000.140-0.108-0.112-0.108
day-0.064-0.0090.0010.1401.000-0.058-0.092-0.088
duration0.029-0.0330.043-0.108-0.0581.0000.0290.031
pdays0.494-0.0170.070-0.112-0.0920.0291.0000.986
previous0.496-0.0120.080-0.108-0.0880.0310.9861.000

Missing values

2025-02-16T23:09:40.210931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-16T23:09:40.275642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
0058managementmarriedtertiaryno2143yesnounknown5may2611-10unknownno
1144techniciansinglesecondaryno29yesnounknown5may1511-10unknownno
2233entrepreneurmarriedsecondaryno2yesyesunknown5may761-10unknownno
3347blue-collarmarriedunknownno1506yesnounknown5may921-10unknownno
4433unknownsingleunknownno1nonounknown5may1981-10unknownno
5535managementmarriedtertiaryno231yesnounknown5may1391-10unknownno
6628managementsingletertiaryno447yesyesunknown5may2171-10unknownno
7742entrepreneurdivorcedtertiaryyes2yesnounknown5may3801-10unknownno
8858retiredmarriedprimaryno121yesnounknown5may501-10unknownno
9943techniciansinglesecondaryno593yesnounknown5may551-10unknownno
Unnamed: 0agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
452014520153managementmarriedtertiaryno583nonocellular17nov22611844successyes
452024520234admin.singlesecondaryno557nonocellular17nov2241-10unknownyes
452034520323studentsingletertiaryno113nonocellular17nov2661-10unknownyes
452044520473retiredmarriedsecondaryno2850nonocellular17nov3001408failureyes
452054520525techniciansinglesecondaryno505noyescellular17nov3862-10unknownyes
452064520651technicianmarriedtertiaryno825nonocellular17nov9773-10unknownyes
452074520771retireddivorcedprimaryno1729nonocellular17nov4562-10unknownyes
452084520872retiredmarriedsecondaryno5715nonocellular17nov112751843successyes
452094520957blue-collarmarriedsecondaryno668nonotelephone17nov5084-10unknownno
452104521037entrepreneurmarriedsecondaryno2971nonocellular17nov361218811otherno